Fine-Grained Sleep Apnea Detection Method from Multichannel Ballistocardiogram Using Convolution Neural Network - HUANG Yongfeng; HUANG Qihong; SUN Chenxi; YANG Shuchen; ZHANG Zhiming (东华大学学报（英文版）, 2023)
TP399; Sleep apnea is a common health condition that can affect numerous aspects of life and may cause a lot of health problems especially in the middle-aged and elderly population.Polysomnography(PSG),as the gold standard,is an expensive and inconvenient way to diagnose sleep apnea.However,ballistocardiogram can be collected by devices embedded in the surrounding environment,enabling inperceptible sleep apnea detection.Moreover,to obtain the fine-grained apnea fragments,a multistage sleep apnea detection model has been proposed.This model firstly uses an improved convolution neural network(CNN)model to coarsely identify apnea events and then a U-Net based model is applied to finely segment apnea fragments.In the experiment,sleep data of 11 patients with apnea for about 70 h have been collected,including BCG data derived from 18 piezoelectric polyvinylidene fluoride(PVDF)sensors embedded in the mattress and PSG data collected synchronously.The results show the accuracy of the classification model as good as 95.7％with 0.818 dice coefficient of the segmentation model,which indicates that the proposed model can almost match the performance of PSG in detecting apnea.